Borrowing it
Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/CLAUDE.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/claude-md)<a href="https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/claude-md"><img src="https://agentmods.dev/badge/instructions/pskoett/measuring-ai-proficiency/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/pskoett/measuring-ai-proficiency/claude-md"><img src="https://agentmods.dev/badge/instructions/pskoett/measuring-ai-proficiency/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04005 | $0.04005 |
| Opus 5 | $0.02003 | $0.02003 |
| Sonnet 5 | $0.00801 | $0.00801 |
| Haiku 4.5 | $0.00400 | $0.00400 |
Grade A, and why
measuring-ai-proficiency CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instructions
Workflow Orchestration
1. Plan Mode Default
- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
- If something goes sideways, STOP and re-plan immediately - don't keep pushing
- Use plan mode for verification steps, not just building
- Write detailed specs upfront to reduce ambiguity
2. Subagent Strategy
- Use subagents liberally to keep main context window clean
- Offload research, exploration, and parallel analysis to subagents
- For complex problems, throw more compute at it via subagents
- One task per subagent for focused execution
3. Self-Improvement Loop
- After ANY correction from the user: use
/self-improvementto log: - Write rules for yourself that prevent the same mistake
- Ruthlessly iterate on these lessons until mistake rate drops
- Review lessons at session start for relevant project
Self-Improvement Workflow
When errors or corrections occur:
- Log to
.learnings/ERRORS.md,LEARNINGS.md, orFEATURE_REQUESTS.md - Review and promote broadly applicable learnings to:
CLAUDE.md- project facts and conventionsAGENTS.md- workflows and automation.github/copilot-instructions.md- Copilot context
- If the user requests a commit cadence (for example, "commit after each iteration"), create a commit at the end of each completed iteration before asking for the next test.
4. Verification Before Done
- Never mark a task complete without proving it works
- Diff behavior between main and your changes when relevant
- Ask yourself: "Would a staff engineer approve this?"
- Run tests, check logs, demonstrate correctness
5. Demand Elegance (Balanced)
- For non-trivial changes: pause and ask "is there a more elegant way?"
- If a fix feels hacky: "Knowing everything I know now, implement the elegant solution"
- Skip this for simple, obvious fixes - don't over-engineer
- Challenge your own work before presenting it
6. Autonomous Bug Fixing
- When given a bug report: just fix it. Don't ask for hand-holding
- Point at logs, errors, failing tests - then resolve them
- Zero context switching required from the user
- Go fix failing CI tests without being told how
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 321 lines · 4,005 tokens per session scan A d263c3116920
measuring-ai-proficiency CLAUDE.md is an instructions file published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 4,005 tokens to every session, about $0.0200 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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